The development of a novel shade selection program for fixed shade translucent dental materials

The objective of this study was to develop a shade selection program that predicts the shade choice with the smallest CIEDE2000 color difference for dental composite resin restorations when given a backing and target shade. By utilizing previously generated regression models, a database of spectral reflectance information, and principles of Kubelka-Munk layering, a highly accurate shade selection program was designed.

Using SAS 9.3 Statistical Analysis Software, a shade selection program was developed that incorporated Kubelka-Munk layering data of Herculite Ultra and Estelite Omega composite resins from a characterized database of absorption and scattering information. Test scenarios represented an inquiry based off of a backing selection shade (Shade B) and a target selection shade (ShadeT). For the simulation, the thickness of the layer on the backing was 1.9mm and the CIE illuminant was D65. The selection program was designed to select the shade that would give the lowest CIEDE2000 color difference when selected for the specific target and backing. When using the 3-D Vita shade guide, analysis between direct reflectance data and RGB data regressed to reflectance data was included in order to verify accuracy of the regression.

Test scenarios indicated a systematic and accurate shade selection system by suggesting a shade that resulted in a CIEDE2000 color difference of zero when using the same target and backing shades. Most scenarios of backing and target combinations gave at least one option that was beneath the acceptability threshold indicating a clinically acceptable shade match. Many test scenarios indicated options that were beneath the perceptibility threshold indicating a highly accurate process of shade selection. There was generally little variability in the CIEDE2000 color differences when using the reflectance data versus the RGB regression data as input into the shade selection program further verifying the accuracy of a previously generated regression model.

The shade selection program that was developed is a viable system that could reduce variability in observer selections while increasing patient satisfaction for potential use in clinical situations that require color matching a restoration to a tooth.